--- title: "Maverick: Efficient and Accurate — Martinelli 2024" description: "Recent state-of-the-art coreference resolution systems rely on huge generative Sequence-to-Sequence models (up to 13B parameters)." authors: ["Giuliano Martinelli", "Edoardo Barba", "Roberto Navigli"] year: 2024 venue: "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)" source: "https://aclanthology.org/2024.acl-long.722/" doi: "10.18653/v1/2024.acl-long.722" arxiv: "2407.21489" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/martinelli-2024-maverick-efficient-accurate-coreference-resolution/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2024](https://md.research.elamur.ai/free/en/papers/2024/page/3/) / Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends # Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends **Authors:** Giuliano Martinelli, [Edoardo Barba](https://md.research.elamur.ai/free/en/authors/edoardo-barba/), [Roberto Navigli](https://md.research.elamur.ai/free/en/authors/roberto-navigli/) · **Year:** 2024 · **Venue:** Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2024.acl-long.722/) · [PDF](https://aclanthology.org/2024.acl-long.722.pdf) · [DOI 10.18653/v1/2024.acl-long.722](https://doi.org/10.18653/v1/2024.acl-long.722) · [arXiv 2407.21489](https://arxiv.org/abs/2407.21489) · [OpenAlex](https://openalex.org/W4402671132) **Citations:** 11 citations (openalex-snapshot · 2026-10-14) · [25](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary Recent state-of-the-art coreference resolution systems rely on huge generative Sequence-to-Sequence models (up to 13B parameters). These models are slow, need a lot of memory, and are out of reach for most academic groups. The authors argue that encoder-only discriminative approaches were abandoned too soon. They present Maverick, a redesigned training and preprocessing pipeline that replaces the classic Coarse-to-Fine setup. Instead of scoring every possible span, it first predicts mention starts and then, for each start, predicts possible ends. It adds mention regularization and a new pruning strategy, which removes the need for hand-tuned memory thresholds. On top of this pipeline they build three systems: two mention-antecedent models (Maverick s2e, Maverick mes) and a new incremental model (Maverick incr), in which a lightweight transformer attends over previously clustered mentions. With about 500M parameters, Maverick achieves state-of-the-art results on CoNLL-2012 (English OntoNotes) and beats much larger generative models. It also trains on a single RTX 4090 and runs inference far faster. This makes strong coreference resolution practical for downstream applications and low-budget research. ## Key points - Maverick beats generative systems of up to 13B parameters (e.g., Link-Append, ASP) with roughly 500M parameters, using DeBERTa-large as the encoder. - Efficiency compared with Link-Append: about 0.006x the training memory (175x less) and up to 170x faster inference. It is also about 85x faster than ASP. Training fits under 18GB of VRAM, while earlier Coarse-to-Fine models needed 32GB. - In the ablation, the new incremental formulation (Maverick incr) gains +3.9 CoNLL-F1 over the older ICoref/longdoc-style incremental method (83.5 vs 79.6). However, it is slower in both training and inference. - With DeBERTa, Maverick s2e and Maverick mes outperform s2e-coref and LingMess trained on the same encoder. The pipeline's efficiency gains do not depend on which encoder is used. - Experiments covered English only, but the method is presented as language-agnostic. Code and models are available at github.com/SapienzaNLP/maverick-coref. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/) Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) ## Cited by Cited by 25 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 23 not yet. - [Reverse Probing: Evaluating Knowledge Transfer via Finetuned Task Embeddings for Coreference Resolution](https://md.research.elamur.ai/free/en/papers/anikina-2025-reverse-probing-evaluating-knowledge-transfer/) - [Findings of the Fifth Shared Task on Multilingual Coreference Resolution: Expanding Datasets for Long-Range Entities](https://md.research.elamur.ai/free/en/papers/novak-2026-findings-fifth-shared-task-multilingual/) ## Cite The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{martinelli2024maverick, title = {{Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends}}, author = {Martinelli, Giuliano and Barba, Edoardo and Navigli, Roberto}, booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, year = {2024}, doi = {10.18653/v1/2024.acl-long.722}, eprint = {2407.21489}, archivePrefix = {arXiv}, url = {https://doi.org/10.18653/v1/2024.acl-long.722} } ``` - [← previous MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?](https://md.research.elamur.ai/free/en/papers/zhang-2024-mathverse-multi-modal-llm-truly/) - [next → Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset](https://md.research.elamur.ai/free/en/papers/wang-2024-measuring-multimodal-mathematical-reasoning-math/) - [↑ back to papers of 2024](https://md.research.elamur.ai/free/en/papers/2024/page/3/) ## References 51 references, 33 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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